Utilizing machine learning with self-support actions to determine support queue positions for support calls

ABSTRACT

A device receives a communication associated with a support issue encountered by a user, and receives information identifying one or more self-support actions performed by the user in relation to the support issue. The device assigns the communication to a position in a support queue. The support queue includes information identifying positions of other communications received from other users, when the other communications are received, and self-support actions performed by the other users. The device associates the information identifying the one or more self-support actions with information identifying the position of the communication, and applies respective weights to the one or more self-support actions. The device generates a score for the communication based on applying the respective weights, and modifies the position of the communication based on the score. The device performs one or more actions based on modifying the position of the communication.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/359,430, filed Mar. 20, 2019, which is a continuation of U.S. patentapplication Ser. No. 16/180,859, filed Nov. 5, 2018 (now U.S. Pat. No.10,263,862), which are incorporated herein by reference.

BACKGROUND

User or customer self-support or self-service is a blend ofuser-initiated interaction technologies that are designed to enableusers to provide support and/or services for themselves. Self-supportmay include providing computer-generated instructions fortroubleshooting an issue, utilizing electronic records managementsystems, utilizing computer-generated chat and knowledge bases, and/orthe like.

SUMMARY

According to some implementations, a method may include receiving, froma user device, a communication associated with a support issueencountered by a user of the user device, and receiving, from the userdevice, information identifying one or more self-support actionsperformed by the user for the support issue. The method may includeassigning the communication to a position in a support queue based onwhen the communication is received, wherein the support queue mayinclude information identifying positions of other communicationsreceived from other users, and information identifying when the othercommunications are received. The method may include associating theinformation identifying the one or more self-support actions performedby the user with information identifying the position of thecommunication in the support queue, and associating the informationidentifying the self-support actions performed by the other users withthe information identifying the positions of the other communications inthe support queue. The method may include processing the informationidentifying the one or more self-support actions performed by the userand the information identifying the self-support actions performed bythe other users, with a machine learning model, to generate respectiveweights for the one or more self-support actions performed by the userand for the self-support actions performed by the other users. Themethod may include associating the respective weights with the one ormore self-support actions performed by the user and with theself-support actions performed by the other users, and generating scoresfor the communication and the other communications based on associatingthe respective weights with the one or more self-support actionsperformed by the user and with the self-support actions performed by theother users. The method may include modifying the position of thecommunication and the positions of the other communications, in thesupport queue, based on the scores, and performing one or more actionsbased on modifying the position of the communication and the positionsof the other communications in the support queue.

According to some implementations, a device may include one or morememories, and one or more processors, communicatively coupled to the oneor more memories, to receive, from a user device, a communicationassociated with a support issue encountered by a user of the userdevice, and receive information identifying one or more self-supportactions performed by the user in relation to the support issue. The oneor more processors may assign the communication to a position in asupport queue based on when the communication is received, wherein thesupport queue may include information identifying positions of othercommunications received from other users, information identifying whenthe other communications are received, and information identifyingself-support actions performed by the other users. The one or moreprocessors may associate the information identifying the one or moreself-support actions performed by the user with information identifyingthe position of the communication in the support queue, and may applyrespective weights to the one or more self-support actions performed bythe user. The one or more processors may generate a score for thecommunication based on applying the respective weights to the one ormore self-support actions performed by the user, and may modify theposition of the communication in the support queue based on the score.The one or more processors may perform one or more actions based onmodifying the position of the communication in the support queue.

According to some implementations, a non-transitory computer-readablemedium may store instructions that include one or more instructionsthat, when executed by one or more processors of a device, cause the oneor more processors to receive communications associated with supportissues encountered by users of user devices, and receive, from the userdevices, information identifying self-support actions performed by theusers in relation to the support issues. The one or more instructionsmay cause the one or more processors to assign the communications topositions in a support queue based on when the communications arereceived, wherein the support queue may include information identifyingthe positions of the communications, and information identifying whenthe communications are received. The one or more instructions may causethe one or more processors to associate the information identifying theself-support actions performed by the users with the informationidentifying the positions of the communications, and process theinformation identifying the self-support actions performed by the users,with a model, to generate respective weights for the self-supportactions performed by the users. The one or more instructions may causethe one or more processors to associate the respective weights with theself-support actions performed by the users, and generate scores for thecommunications based on associating the respective weights with theself-support actions performed by the users. The one or moreinstructions may cause the one or more processors to modify thepositions of the communications, in the support queue, based on thescores, and perform one or more actions based on modifying the positionsof the communications in the support queue.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1K are diagrams of an example implementation described herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2.

FIGS. 4-6 are flow charts of example processes for utilizing machinelearning with self-support actions to determine support queue positionsfor support calls.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

Self-support options exist for troubleshooting problems about which auser calls a service center. However, there is little incentive for theuser to utilize such self-service options, aside from avoiding the callto the service center. Furthermore, once a user has called the servicecenter, the user typically does not perform the self-service options.

Some implementations described herein provide a support platform thatutilizes machine learning with self-support actions to determine supportqueue positions for support calls. For example, the support platform mayreceive, from a user device, a communication associated with a supportissue encountered by a user of the user device, and may receiveinformation identifying one or more self-support actions performed bythe user in relation to the support issue. The support platform mayassign the communication to a position in a support queue based on whenthe communication is received. The support queue may include informationidentifying positions of other communications received from other users,information identifying when the other communications were received, andinformation identifying self-support actions performed by the otherusers. The support platform may associate the information identifyingthe one or more self-support actions performed by the user withinformation identifying the position of the communication in the supportqueue, and may apply respective weights to the one or more self-supportactions performed by the user. The support platform may generate a scorefor the communication based on applying the respective weights, and maymodify the position of the communication in the support queue based onthe score. The support platform may perform one or more actions based onmodifying the position of the communication in the support queue.

In this way, the support platform may provide incentives for a user totry to solve problems using automated self-support tools or actions. Thesupport platform may reduce a waiting time for a support call based onutilizing the automated self-support actions and based on a quantity ofself-support actions performed by the user. The support platform mayencourage users to utilize self-support actions rather than calling aservice center, which may conserve resources (e.g., processingresources, memory resources, and/or the like) associated with processingservice and/or support calls by the service center.

FIGS. 1A-1K are diagrams of an example implementation 100 describedherein. As shown in FIGS. 1A-1K, a user device may be associated with afirst user (e.g., User 1, a customer seeking customer support) and asupport platform. The first user of the user device may utilize the userdevice to establish and/or provide a communication (e.g., a call, aninstant message of an instant messaging session, video of a videosession, and/or the like) with the support platform. In someimplementations, the communication may be associated with a supportissue (e.g., a network outage, a service interruption, a user deviceissue, and/or the like) of the first user that is to be resolved by anentity associated with the support platform. As shown in FIG. 1A, and byreference number 105, the support platform may receive, from the userdevice, the communication associated with the support issue of the firstuser.

As further shown in FIG. 1A, and by reference number 110, the supportplatform may assign the call of the first user to a position in asupport queue (e.g., based on when the call is received by the supportplatform). In some implementations, the support queue may include aranked list of communications (e.g., calls) received by the supportplatform from the first user and other users (e.g., associated withother user devices). In some implementations, the support platform mayrank the calls in the ranked list based on when the calls were receivedby the support platform. For example, an oldest call (e.g., a call thatis received the earliest by the support platform) may be ranked first inthe support queue, followed by a next oldest call, and/or the like. Insome implementations, the support queue may be stored in a datastructure (e.g., a database, a table, a list, and/or the like)associated with the support platform. In some implementations, thesupport platform may address the calls in the support queue based on therankings of the calls (e.g., with a first ranked call being addressedfirst, followed by a second ranked call, and/or the like).

As further shown in FIG. 1A, and by reference number 115, the supportplatform may provide, to the user device, information identifyingself-support actions that the first user may perform in order to improvethe position of the call of the first user in the support queue. In someimplementations, the support platform may determine the self-supportactions that the first user may perform based on the support issue ofthe first user, and may provide, to the user device, informationidentifying the determined self-support actions. For example, if thesupport issue relates to a network connectivity issue, the self-supportactions may include powering a router off and on, powering a modem offand on, checking an optical network terminal (ONT), checking for a poweroutage, and/or the like. In some implementations, the support platformmay automatically perform one or more of the determined self-supportactions (e.g., with user permission) before recommending theself-support actions to the first user. In such implementations, thesupport platform may or may not recommend the determined self-supportactions that were automatically performed by the support platform. Insome implementations, the support platform may determine theself-support actions that the first user may perform based oninformation associated with the first user. For example, the first usermay have an account and a user profile established with the supportplatform, and the support platform may utilize the account and the userprofile to identify potential issues associated with the account and/orthe user profile (e.g., a malfunctioning router) and to determine theself-support actions that the first user may perform to correct thepotential issues.

With reference to FIG. 1B, the first user may utilize the user deviceand/or another user device to perform self-support actions. In someimplementations, the first user may utilize the user device and/or theother device to perform one or more of the self-support actionsrecommended by the support platform, as described above in connectionwith FIG. 1A. As shown in FIG. 1B, and by reference number 120, thesupport platform may receive, from the user device and/or the other userdevice, information identifying the self-support actions performed bythe first user and a telephone number associated with the first user(e.g., associated with the user device of the first user).

In some implementations, the first user may not have an account with thesupport platform and may perform the self-support actions with thesupport platform via an unauthenticated session. In suchimplementations, the support platform may utilize the telephone numberassociated with the first user to correlate the self-support actionsperformed by the first user with the position of the call in the supportqueue. In some implementations, the first user may have an authenticatedaccount with the support platform and may perform the self-supportactions with the support platform via the authenticated account. In suchimplementations, the support platform may recognize the telephone numberassociated with the first user, based on the authenticated account, andmay utilize the authenticated account and/or the telephone number tocorrelate the self-support actions performed by the first user with theposition of the call in the support queue.

As further shown in FIG. 1B, and by reference number 125, the supportplatform may modify the position of the call of the first user in thesupport queue based on the self-support actions performed by the firstuser. In some implementations, the support platform may upgrade theposition of the call (e.g., from a fourth-ranked call to a second-rankedcall) in the support queue based on the self-support actions performedby the first user, may maintain the position of the call (e.g., as thefourth-ranked call) in the support queue based on the self-supportactions performed by the first user, may downgrade the position of thecall (e.g., from a fourth-ranked call to a fifth-ranked call) in thesupport queue based on the self-support actions performed by the firstuser, and/or the like. Further details associated with modifyingpositions of calls in the support queue are provided below in connectionwith FIGS. 1E-1I.

With reference to FIG. 1C, a second user may be associated with two userdevices (e.g., a computer and a mobile telephone). In someimplementations, the second user may include an authenticated accountwith the support platform and may perform self-support actions (e.g.,associated with a support issue) with the support platform via theauthenticated account and one of the user devices. As shown in FIG. 1C,and by reference number 130, the support platform may receive, from oneof the user devices, information identifying the self-support actionsperformed by the second user via the authenticated account.

As further shown in FIG. 1C, and by reference number 135, the supportplatform may receive, from one of the user devices, a call associatedwith the support issue of the second user after the user performs theself-support actions associated with the support issue via theauthenticated account. In some implementations, the support platform mayrecognize the telephone number associated with the second user (e.g.,one of the user devices), based on the authenticated account, and mayutilize the authenticated account and/or the telephone number tocorrelate the self-support actions performed by the second user with thecall in the support queue.

As further shown in FIG. 1C, and by reference number 140, the supportplatform may assign the call of the second user to a position in thesupport queue based on when the call was received by the supportplatform, based on the self-support actions performed by the seconduser, and/or the like. In some implementations, the support platform mayassign the call of the second user to the position in the support queuebased on when the call was received, and may modify the position of thecall in the support queue based on the self-support actions performed bythe second user. Further details associated with modifying positions ofcalls in the support queue are provided below in connection with FIGS.1E-1I.

With reference to FIG. 1D, a third user may be associated with userdevices (e.g., a computer and a mobile telephone). In someimplementations, the third user may not include an authenticated accountwith the support platform but may perform self-support actions (e.g.,associated with a support issue) with the support platform via anunauthenticated account. As shown in FIG. 1D, and by reference number145, the support platform may receive, from one of the user devices,information identifying the self-support actions performed by the thirduser via the unauthenticated account.

As further shown in FIG. 1D, and by reference number 150, the supportplatform may provide, to one of the user devices, an identifier foridentifying the self-support actions performed by the third user via theunauthenticated account. In some implementations, the identifier mayinclude a number, alphanumeric characters, alphabetical characters, apasscode, a password, a pass phrase, and/or the like.

As further shown in FIG. 1D, and by reference number 155, the supportplatform may receive, from one of the user devices, a call associatedwith the support issue of the third user after the user performs theself-support actions associated with the support issue via theunauthenticated account. In some implementations, the support platformmay not recognize the telephone number associated with the third user(e.g., one of the user devices), since the third user does not have anauthenticated account. Therefore, the support platform may request thatthe third user provide (e.g., via one of the user devices) theidentifier for identifying the self-support actions performed by thethird user. The support platform may receive, from one of the userdevices, the identifier identifying the self-support actions performedby the third user based on the request. In some implementations, thesupport platform may utilize the identifier to correlate theself-support actions performed by the third user with the call in thesupport queue.

As further shown in FIG. 1D, and by reference number 160, the supportplatform may assign the call of the third user to a position in thesupport queue based on when the call was received by the supportplatform, based on the self-support actions performed by the third user,and/or the like. In some implementations, the support platform mayassign the call of the third user to the position in the support queuebased on when the call was received, and may modify the position of thecall in the support queue based on the self-support actions performed bythe third user. Further details associated with modifying positions ofcalls in the support queue are provided below in connection with FIGS.1E-1I.

As shown in FIG. 1E, and by reference number 165, the support platformmay associate self-support actions of users with positions of calls inthe support queue. In some implementations, the support platform mayassociate the self-support actions of the users with the positions ofthe calls in the support queue as described above in connection withFIGS. 1A-1D. As further shown in FIG. 1E, the support queue may includea position field, a caller field, a number of actions field, a timespent field, an action types field, and a variety of entries associatedwith the fields. These are simply examples of fields that may beincluded in the support queue. In some implementations, the supportqueue may include fewer or additional fields. For example, one or moreof these fields may be provided in a data structure separate from thesupport queue.

The position field may include information indicating positions of callswithin the support queue. For example, the position field may include aranking number for each of the calls identified in the support queue. Insome implementations, a ranking number of one (1) may indicate a highestranking and the next call to be serviced, two (2) may indicate a nexthighest ranking and the call to be serviced after the highest-rankedcall, and/or the like. In some implementations, the support queue maynot include a position field and a ranking of a call within the supportqueue may be determined based on where the call is identified within thesupport queue (e.g., a call that is identified closer to a front (ortop) of the support queue may be ranked higher than another call that isidentified farther from the front (or top) of the support queue).

The caller field may include information identifying callers (e.g.,users) associated with calls (e.g., or other communications) provided tothe support platform. For example, the caller field may indicate thatthe second user (User 2) is associated with the first-ranked call, thefirst user (User 1) is associated with the second-ranked call, a fourthuser (User 4) is associated with the third-ranked call, the third user(User 3) is associated with the fourth-ranked call, and/or the like.

The number of actions field may include information identifying aquantity of self-support actions performed by the users identified inthe caller field. For example, the number of actions field may indicatethat the second user performed five self-support actions, the first userperformed zero self-support actions, the fourth user performed threeself-support actions, the third user performed two self-support actions,and/or the like.

The time spent field may include information identifying an amount oftime waiting for a call to be serviced; an amount of time spent, by theusers identified in the caller field, performing the self-supportactions; and/or the like. For example, the time spent field may indicatethat the second user spent ten minutes waiting for service and/orperforming the self-support actions, the first user spent fifteenminutes waiting for service, the fourth user spent seven minutes waitingfor service and/or performing the self-support actions, the third userspent eight minutes waiting for service and/or performing theself-support actions, and/or the like.

The action types field may include information identifying the types ofself-support actions performed by the users identified in the callerfield. For example, the action types field may indicate that the seconduser performed Type 1 self-support actions, the first user performed noself-support actions, the fourth user performed Type 2 self-supportactions, the third user performed Type 3 self-support actions, and/orthe like. In some implementations, a user may perform multipleself-support actions that are of different types. For example, thesecond user may perform five actions of Types 1, 2, and 3.

Although FIG. 1E depicts particular information in the support queue, insome implementations, the support queue may include additionalinformation, different information, less information, and/or the like.

As shown in FIG. 1F, and by reference number 170, the support platformmay process information identifying different self-support actions andtime spent performing the different self-support actions (e.g., providedin the support queue) with a machine learning model, to determinerespective weights for the different self-support actions and/or thetime spent performing the different self-support actions. In someimplementations, the machine learning model may include a patternrecognition model that generates the respective weights for thedifferent self-support actions and/or the time spent performing thedifferent self-support actions.

In some implementations, the support platform may perform a trainingoperation on the machine learning model, with historical self-supportinformation associated with previously performed self-support actions.The historical self-support information may include informationindicating difficulties associated with the self-support actions, timespent performing the self-support actions, results of performing theself-support actions (e.g., support issues resolved, almost resolved,not resolved, etc.), time savings by support personnel based on theself-support actions (e.g., when users performed self-support action X,it resulted in less time spent by support personnel troubleshooting asupport issue, which conserves resources), and/or the like.

The support platform may separate the historical self-supportinformation into a training set, a validation set, a test set, and/orthe like. The training set may be utilized to the train the machinelearning model. The validation set may be utilized to validate resultsof the trained machine learning model. The test set may be utilized totest operations of the machine learning model. In some implementations,the support platform may train the machine learning model using, forexample, an unsupervised training procedure and based on the historicalself-support information. For example, the support platform may performdimensionality reduction to reduce the historical self-supportinformation to a minimum feature set, thereby reducing resources (e.g.,processing resources, memory resources, and/or the like) to train themachine learning model, and may apply a classification technique to theminimum feature set.

In some implementations, the support platform may use a logisticregression classification technique to determine a categorical outcome(e.g., that the historical self-support information indicates certainresults). Additionally, or alternatively, the support platform may use anaïve Bayesian classifier technique. In this case, the support platformmay perform binary recursive partitioning to split the historicalself-support information into partitions and/or branches, and use thepartitions and/or branches to perform predictions (e.g., that thehistorical self-support information indicates certain results). Based onusing recursive partitioning, the support platform may reduceutilization of computing resources relative to manual, linear sortingand analysis of data points, thereby enabling use of thousands,millions, or billions of data points to train the machine learningmodel, which may result in a more accurate model than using fewer datapoints.

Additionally, or alternatively, the support platform may use a supportvector machine (SVM) classifier technique to generate a non-linearboundary between data points in the training set. In this case, thenon-linear boundary is used to classify test data into a particularclass.

Additionally, or alternatively, the support platform may train themachine learning model using a supervised training procedure thatincludes receiving input to the machine learning model from a subjectmatter expert, which may reduce an amount of time, an amount ofprocessing resources, and/or the like to train the machine learningmodel relative to an unsupervised training procedure. In someimplementations, the support platform may use one or more other modeltraining techniques, such as a neural network technique, a latentsemantic indexing technique, and/or the like. For example, the supportplatform may perform an artificial neural network processing technique(e.g., using a two-layer feedforward neural network architecture, athree-layer feedforward neural network architecture, and/or the like) toperform pattern recognition with regard to patterns of the historicalself-support information. In this case, using the artificial neuralnetwork processing technique may improve an accuracy of the trainedmachine learning model generated by the support platform by being morerobust to noisy, imprecise, or incomplete data, and by enabling thesupport platform to detect patterns and/or trends undetectable to humananalysts or systems using less complex techniques.

As shown in FIG. 1G, and by reference number 175, the support platformmay associate the respective weights with the different self-supportactions and/or the time spent performing the different self-supportactions. In some implementations, different self-support actions mayinclude different weights. For example, if a self-support actiontypically solves a support issue, the self-support action may beallotted a greater weight than another self-support action that does nottypically solve a support issue. In another example, if a self-supportaction requires a greater amount of time to perform than anotherself-support action, the self-support action may be allotted a greaterweight than the other self-support action. In still another example, ifa self-support action conserves more resources for the support platformthan another self-support action, the self-support action may beallotted a greater weight than the other self-support action.

As further shown in FIG. 1G, the support queue may include a weightsfield that provides information identifying the respective weightsassociated with the different self-support actions and/or the time spentperforming the different self-support actions. For example, the weightsfield may indicate that weights of 0.1 and 0.2 are associated with thedifferent self-support actions and/or the time spent performing thedifferent self-support actions by the second user, a weight of 0.0 isassociated with the different self-support actions and/or the time spentperforming the different self-support actions by the first user, weightsof 0.5 and 0.5 are associated with the different self-support actionsand/or the time spent performing the different self-support actions bythe fourth user, a weight of 0.7 is associated with the differentself-support actions and/or the time spent performing the differentself-support actions by the third user, and/or the like. In someimplementations, a user may perform multiple self-support actions andthe same or different weights may be associated with each of themultiple self-support actions.

As shown in FIG. 1H, and by reference number 180, the support platformmay generate scores for the calls based on the respective weightsassociated with the different self-support actions and/or the time spentperforming the different self-support actions. In some implementations,different calls may include different scores based on the respectiveweights associated with the different self-support actions and/or thetime spent performing the different self-support actions. For example,if a call is associated with one or more respective weights that aregreater than one or more respective weights associated with anothercall, the call may receive a higher score than the other call. Inanother example, if a call is associated with one or more respectiveweights that are less than one or more respective weights associatedwith another call, the call may receive a lower score than the othercall.

As further shown in FIG. 1H, the support queue may include a scoresfield that provides information identifying the scores generated forrespective calls. For example, the scores field may indicate that thecall associated with the second user has a score of 0.9, the callassociated with the first user has a score of 0.7, the call associatedwith the fourth user has a score of 0.6, the call associated with thethird user has a score of 0.8, and/or the like.

As shown in FIG. 1I, and by reference number 185, the support platformmay modify positions of the calls in the support queue based on thescores generated for the calls based on the respective weights. In someimplementations, the support platform may re-rank the calls in thesupport queue based on the scores generated for the calls. In suchimplementations, a call associated with a greatest score may be rankedfirst (e.g., position 1), a call associated with a next greatest scoremay be ranked second (e.g., position 2), and/or the like. For example,as shown in FIG. 1I, and with reference to FIG. 1H, the call associatedwith the third user may be moved from the fourth position to the secondposition (e.g., since the call has a score of 0.8), the call associatedwith the first user may be moved from the second position to the thirdposition (e.g., since the call has a score of 0.7), and the callassociated with the fourth user may be moved from the third position tothe fourth position (e.g., since the call has a score of 0.6).

As shown in FIG. 1J, and by reference number 190, the support platformmay perform one or more actions based on modifying the positions of thecalls in the support queue (e.g., based on the scores). For example, theone or more actions may include the support platform informing a user(e.g., via a user device) that the user's position in the support queueimproved due to the user performing self-support actions. In this way,the user may be encouraged to continue performing the self-supportactions and resolve the support issue, which may conserve resources thatwould otherwise be wasted in attempting to resolve the support issuewithout the user having performed the self-support actions. Furthermore,as more self-support actions are performed by the user, more data iscollected for the machine learning model and resolution of a supportissue may be more quickly achieved.

In some implementations, the one or more actions may include the supportplatform processing a call of a user based on modifying a position ofthe call. For example, the position of the call may be moved to the topof the support queue and may be immediately processed. In this way, thecall may be handled sooner than if the position of the call was notmodified, which may conserve resources on the user device that wouldotherwise be wasted waiting in the support queue and may conservenetwork resources that would otherwise be wasted to maintain the call.

In some implementations, the one or more actions may include the supportplatform suggesting self-support actions to a user (e.g., a user device)to improve a position of the call in the support queue. In this way, thesupport platform may encourage the user to perform self-support actionsand potentially resolve the support issue, which may conserve resourcesthat would otherwise be wasted in attempting to resolve the supportissue without the user having performed the self-support actions.Furthermore, as more self-support actions are performed by the user,more data is collected for the machine learning model and resolution ofa support issue may be more quickly achieved.

In some implementations, the one or more actions may include the supportplatform disconnecting a call with a user (e.g., a user device) whenself-support actions performed by the user solved a support issue. Inthis way, the support platform may conserve resources (e.g., processingresources, memory resources, network resources, and/or the like)associated with unnecessarily processing a call.

In some implementations, the one or more actions may include the supportplatform providing (e.g., to user devices associated with users)information indicating positions of the calls in the support queue. Inthis way, the positions of the calls may encourage the users to performself-support actions to improve the positions, which may conserveresources that would otherwise be wasted in attempting to resolve thesupport issue without the users having performed the self-supportactions. Furthermore, as more self-support actions are performed by theusers, more data is collected for the machine learning model andresolutions of support issues may be more quickly achieved.

In some implementations, the one or more actions may include the supportplatform causing a device (e.g., a non-operational problem device in anetwork) to reboot or execute a self-diagnostic action. In this way, thesupport platform may resolve a support issue associated with the device,which may conserve resources on the user device that would otherwise bewasted waiting in the support queue and may conserve network resourcesthat would otherwise be wasted to maintain the call.

In some implementations, the one or more actions may include the supportplatform causing a device to attempt to communicate with another device(e.g., a non-operational problem device) and/or causing the device toattempt to diagnose or repair the other device. In this way, the supportplatform may attempt to resolve a support issue associated with theother device, which may conserve resources that would otherwise bewasted in attempting to resolve the support issue without the usershaving performed the self-support actions.

In some implementations, the one or more actions may include the supportplatform causing an autonomous vehicle to travel to a location of a user(e.g., to provide a technician or the user with tools, diagnosticequipment, repair equipment, replacement equipment, and/or the like). Inthis way, the support platform may take preemptive actions to resolve asupport issue, which may conserve resources on the user device thatwould otherwise be wasted waiting in the support queue and may conservenetwork resources that would otherwise be wasted to maintain the call.

In some implementations, the one or more actions may include the supportplatform causing an unmanned aerial vehicle (UAV) to travel to alocation of the user (e.g., to provide a technician or the user withtools, diagnostic equipment, repair equipment, replacement equipment,and/or the like). In this way, the support platform may take preemptiveactions to resolve a support issue, which may conserve resources on theuser device that would otherwise be wasted waiting in the support queueand may conserve network resources that would otherwise be wasted tomaintain the call.

In some implementations, the support platform may perform the one ormore actions based on positions in the support queue for the calls,inferences of the support issues associated with the calls (e.g.,determined based on the self-support actions performed by the users),and/or the like.

As shown in FIG. 1K, and by reference number 195, the support platformmay provide, to a user device associated with a user, informationidentifying movement in the support queue for the user, points awardedfor self-support actions performed by the user, and/or the like. In someimplementations, the user device may receive the information identifyingthe movement in the support queue for the user, points awarded forself-support actions performed by the user, and/or the like, and maypresent the information via a user interface to the user. For example,as shown in FIG. 1K, the user interface may include informationindicating that the user's current position in the support queue isfifth, a time until the user's call is answered is ten minutes, positionchanges for the user in the support queue is three, time saved by theuser due to self-support actions is three minutes, the user performedfour self-support actions, the user spent four minutes with theself-support actions, the user was awarded six points for theself-support actions, and/or the like.

In this way, several different stages of the process for determiningsupport queue positions for support calls are automated with machinelearning, which may remove human subjectivity and waste from theprocess, and which may improve speed and efficiency of the process andconserve computing resources (e.g., processing resources, memoryresources, and/or the like). Furthermore, implementations describedherein use a rigorous, computerized process to perform tasks or rolesthat were not previously performed or were previously performed usingsubjective human intuition or input. For example, currently there doesnot exist a technique that utilizes machine learning with self-supportactions to determine support queue positions for support calls. Finally,automating the process for determining support queue positions forsupport calls conserves computing resources (e.g., processing resources,memory resources, and/or the like) that would otherwise be wasted inattempting to process support calls.

As indicated above, FIGS. 1A-1K are provided merely as examples. Otherexamples may differ from what is described with regard to FIGS. 1A-1K.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods described herein may be implemented. As shown in FIG. 2,environment 200 may include a user device 210, a support platform 220,and a network 230. Devices of environment 200 may interconnect via wiredconnections, wireless connections, or a combination of wired andwireless connections.

User device 210 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information, such asinformation described herein. For example, user device 210 may include amobile phone (e.g., a smart phone, a radiotelephone, etc.), a laptopcomputer, a tablet computer, a desktop computer, a handheld computer, agaming device, a wearable communication device (e.g., a smartwristwatch, a pair of smart eyeglasses, etc.), or a similar type ofdevice. In some implementations, user device 210 may receive informationfrom and/or transmit information to support platform 220.

Support platform 220 includes one or more devices that utilize machinelearning with self-support actions to determine support queue positionsfor support calls. In some implementations, support platform 220 may bedesigned to be modular such that certain software components may beswapped in or out depending on a particular need. As such, supportplatform 220 may be easily and/or quickly reconfigured for differentuses. In some implementations, support platform 220 may receiveinformation from and/or transmit information to one or more user devices210.

In some implementations, as shown, support platform 220 may be hosted ina cloud computing environment 222. Notably, while implementationsdescribed herein describe support platform 220 as being hosted in cloudcomputing environment 222, in some implementations, support platform 220may be non-cloud-based (i.e., may be implemented outside of a cloudcomputing environment) or may be partially cloud-based.

Cloud computing environment 222 includes an environment that hostssupport platform 220. Cloud computing environment 222 may providecomputation, software, data access, storage, etc. services that do notrequire end-user knowledge of a physical location and configuration ofsystem(s) and/or device(s) that host support platform 220. As shown,cloud computing environment 222 may include a group of computingresources 224 (referred to collectively as “computing resources 224” andindividually as “computing resource 224”).

Computing resource 224 includes one or more personal computers,workstation computers, server devices, and/or other types of computationand/or communication devices. In some implementations, computingresource 224 may host support platform 220. The cloud resources mayinclude compute instances executing in computing resource 224, storagedevices provided in computing resource 224, data transfer devicesprovided by computing resource 224, etc. In some implementations,computing resource 224 may communicate with other computing resources224 via wired connections, wireless connections, or a combination ofwired and wireless connections.

As further shown in FIG. 2, computing resource 224 includes a group ofcloud resources, such as one or more applications (“APPs”) 224-1, one ormore virtual machines (“VMs”) 224-2, virtualized storage (“VSs”) 224-3,one or more hypervisors (“HYPs”) 224-4, and/or the like.

Application 224-1 includes one or more software applications that may beprovided to or accessed by user device 210. Application 224-1 mayeliminate a need to install and execute the software applications onuser device 210. For example, application 224-1 may include softwareassociated with support platform 220 and/or any other software capableof being provided via cloud computing environment 222. In someimplementations, one application 224-1 may send/receive informationto/from one or more other applications 224-1, via virtual machine 224-2.

Virtual machine 224-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 224-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 224-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program, and may support a single process. In someimplementations, virtual machine 224-2 may execute on behalf of a user(e.g., a user of user device 210 or an operator of support platform220), and may manage infrastructure of cloud computing environment 222,such as data management, synchronization, or long-duration datatransfers.

Virtualized storage 224-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 224. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 224-4 may provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 224.Hypervisor 224-4 may present a virtual operating platform to the guestoperating systems, and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 230 includes one or more wired and/or wireless networks. Forexample, network 230 may include a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a local area network (LAN),a wide area network (WAN), a metropolitan area network (MAN), atelephone network (e.g., the Public Switched Telephone Network (PSTN)),a private network, an ad hoc network, an intranet, the Internet, a fiberoptic-based network, and/or the like, and/or a combination of these orother types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may beimplemented within a single device, or a single device shown in FIG. 2may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 may perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to user device 210, support platform 220, and/orcomputing resource 224. In some implementations, user device 210,support platform 220, and/or computing resource 224 may include one ormore devices 300 and/or one or more components of device 300. As shownin FIG. 3, device 300 may include a bus 310, a processor 320, a memory330, a storage component 340, an input component 350, an outputcomponent 360, and/or a communication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 is acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random-access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid-state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 360 includes a component that providesoutput information from device 300 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface,and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for utilizing machinelearning with self-support actions to determine support queue positionsfor support calls. In some implementations, one or more process blocksof FIG. 4 may be performed by a support platform (e.g., support platform220). In some implementations, one or more process blocks of FIG. 4 maybe performed by another device or a group of devices separate from orincluding the support platform, such as a user device (e.g., user device210).

As shown in FIG. 4, process 400 may include receiving, from a userdevice, a communication associated with a support issue encountered by auser of the user device (block 405). For example, the support platform(e.g., using computing resource 224, processor 320, communicationinterface 370, and/or the like) may receive, from a user device, acommunication associated with a support issue encountered by a user ofthe user device, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include receiving, from theuser device, information identifying one or more self-support actionsperformed by the user for the support issue (block 410). For example,the support platform (e.g., using computing resource 224, processor 320,memory 330, communication interface 370, and/or the like) may receive,from the user device, information identifying one or more self-supportactions performed by the user for the support issue, as described abovein connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include assigning thecommunication to a position in a support queue based on when thecommunication is received, wherein the support queue includesinformation identifying positions of other communications received fromother users, and information identifying when the other communicationsare received (block 415). For example, the support platform (e.g., usingcomputing resource 224, processor 320, storage component 340, and/or thelike) may assign the communication to a position in a support queuebased on when the communication is received, as described above inconnection with FIGS. 1A-2. In some implementations, the support queuemay include information identifying positions of other communicationsreceived from other users, and information identifying when the othercommunications are received.

As further shown in FIG. 4, process 400 may include associating theinformation identifying the one or more self-support actions performedby the user with information identifying the position of thecommunication in the support queue (block 420). For example, the supportplatform (e.g., using computing resource 224, processor 320, memory 330,and/or the like) may associate the information identifying the one ormore self-support actions performed by the user with informationidentifying the position of the communication in the support queue, asdescribed above in connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include associating theinformation identifying the self-support actions performed by the otherusers with the information identifying the positions of the othercommunications in the support queue (block 425). For example, thesupport platform (e.g., using computing resource 224, processor 320,storage component 340, and/or the like) may associate the informationidentifying the self-support actions performed by the other users withthe information identifying the positions of the other communications inthe support queue, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include processing theinformation identifying the one or more self-support actions performedby the user and the information identifying the self-support actionsperformed by the other users, with a machine learning model, to generaterespective weights for the one or more self-support actions performed bythe user and for the self-support actions performed by the other users(block 430). For example, the support platform (e.g., using computingresource 224, processor 320, memory 330, and/or the like) may processthe information identifying the one or more self-support actionsperformed by the user and the information identifying the self-supportactions performed by the other users, with a machine learning model, togenerate respective weights for the one or more self-support actionsperformed by the user and for the self-support actions performed by theother users, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include associating therespective weights with the one or more self-support actions performedby the user and with the self-support actions performed by the otherusers (block 435). For example, the support platform (e.g., usingcomputing resource 224, processor 320, storage component 340, and/or thelike) may associate the respective weights with the one or moreself-support actions performed by the user and with the self-supportactions performed by the other users, as described above in connectionwith FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include generating scoresfor the communication and the other communications based on associatingthe respective weights with the one or more self-support actionsperformed by the user and with the self-support actions performed by theother users (block 440). For example, the support platform (e.g., usingcomputing resource 224, processor 320, memory 330, and/or the like) maygenerate scores for the communication and the other communications basedon associating the respective weights with the one or more self-supportactions performed by the user and with the self-support actionsperformed by the other users, as described above in connection withFIGS. 1A-2.

As further shown in FIG. 4, process 400 may include modifying theposition of the communication and the positions of the othercommunications, in the support queue, based on the scores (block 445).For example, the support platform (e.g., using computing resource 224,processor 320, storage component 340, and/or the like) may modify theposition of the communication and the positions of the othercommunications, in the support queue, based on the scores, as describedabove in connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include performing one ormore actions based on modifying the position of the communication andthe positions of the other communications in the support queue (block450). For example, the support platform (e.g., using computing resource224, processor 320, memory 330, communication interface 370, and/or thelike) may perform one or more actions based on modifying the position ofthe communication and the positions of the other communications in thesupport queue, as described above in connection with FIGS. 1A-2.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, when performing the one or more actions, thesupport platform may provide information indicating that the position ofthe communication in the support queue improved due to the one or moreself-support actions performed by the user, may process thecommunication of the user based on modifying the position of thecommunication, may provide, to the user device, suggested self-supportactions to improve the position of the communication in the supportqueue, and/or may disconnect the communication with the user device whenthe one or more self-support actions performed by the user solve thesupport issue.

In some implementations, the support platform may provide, to the userdevice, information identifying movement in the support queue for theposition of the communication of the user, and may provide, to the userdevice, information indicating points awarded to the user for the one ormore self-support actions performed by the user.

In some implementations, the support platform may receive thecommunication associated with the support issue prior to receiving theinformation identifying the one or more self-support actions performedby the user, and the support platform may associate the communicationand the information identifying the one or more self-support actionsperformed by the user based on a device identifier associated with thecommunication.

In some implementations, the support platform may receive thecommunication associated with the support issue after receiving theinformation identifying the one or more self-support actions performedby the user, and may associate the communication and the informationidentifying the one or more self-support actions performed by the userbased on an account associated with the user.

In some implementations, the support platform may receive thecommunication associated with the support issue after receiving theinformation identifying the one or more self-support actions performedby the user, may provide, to the user device, an identifier foridentifying the one or more self-support actions performed by the user,may receive the identifier via the communication, and may associate thecommunication and the information identifying the one or moreself-support actions performed by the user based on the identifier.

In some implementations, the support platform may provide, to the userdevice, suggested self-support actions based on receiving thecommunication, where the one or more self-support actions performed bythe user include one or more of the suggested self-support actions.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 for utilizing machinelearning with self-support actions to determine support queue positionsfor support calls. In some implementations, one or more process blocksof FIG. 5 may be performed by a support platform (e.g., support platform220). In some implementations, one or more process blocks of FIG. 5 maybe performed by another device or a group of devices separate from orincluding the support platform, such as a user device (e.g., user device210).

As shown in FIG. 5, process 500 may include receiving, from a userdevice, a communication associated with a support issue encountered by auser of the user device (block 510). For example, the support platform(e.g., using computing resource 224, processor 320, communicationinterface 370, and/or the like) may receive, from a user device, acommunication associated with a support issue encountered by a user ofthe user device, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 5, process 500 may include receivinginformation identifying one or more self-support actions performed bythe user in relation to the support issue (block 520). For example, thesupport platform (e.g., using computing resource 224, processor 320,communication interface 370, and/or the like) may receive informationidentifying one or more self-support actions performed by the user inrelation to the support issue, as described above in connection withFIGS. 1A-2.

As further shown in FIG. 5, process 500 may include assigning thecommunication to a position in a support queue based on when thecommunication is received, wherein the support queue includesinformation identifying positions of other communications received fromother users, information identifying when the other communications arereceived, and information identifying self-support actions performed bythe other users (block 530). For example, the support platform (e.g.,using computing resource 224, processor 320, memory 330, and/or thelike) may assign the communication to a position in a support queuebased on when the communication is received, as described above inconnection with FIGS. 1A-2. In some implementations, the support queuemay include information identifying positions of other communicationsreceived from other users, information identifying when the othercommunications are received, and/or information identifying self-supportactions performed by the other users.

As further shown in FIG. 5, process 500 may include associating theinformation identifying the one or more self-support actions performedby the user with information identifying the position of thecommunication in the support queue (block 540). For example, the supportplatform (e.g., using computing resource 224, processor 320, storagecomponent 340, and/or the like) may associate the informationidentifying the one or more self-support actions performed by the userwith information identifying the position of the communication in thesupport queue, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 5, process 500 may include applying respectiveweights to the one or more self-support actions performed by the user(block 550). For example, the support platform (e.g., using computingresource 224, processor 320, memory 330, and/or the like) may applyrespective weights to the one or more self-support actions performed bythe user, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 5, process 500 may include generating a scorefor the communication based on applying the respective weights to theone or more self-support actions performed by the user (block 560). Forexample, the support platform (e.g., using computing resource 224,processor 320, storage component 340, and/or the like) may generate ascore for the communication based on applying the respective weights tothe one or more self-support actions performed by the user, as describedabove in connection with FIGS. 1A-2.

As further shown in FIG. 5, process 500 may include modifying theposition of the communication in the support queue based on the score(block 570). For example, the support platform (e.g., using computingresource 224, processor 320, memory 330, and/or the like) may modify theposition of the communication in the support queue based on the score,as described above in connection with FIGS. 1A-2.

As further shown in FIG. 5, process 500 may include performing one ormore actions based on modifying the position of the communication in thesupport queue (block 580). For example, the support platform (e.g.,using computing resource 224, processor 320, memory 330, communicationinterface 370, and/or the like) may perform one or more actions based onmodifying the position of the communication in the support queue, asdescribed above in connection with FIGS. 1A-2.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, when performing the one or more actions, thesupport platform may provide information indicating that the position ofthe communication in the support queue improved due to the one or moreself-support actions performed by the user, may process thecommunication of the user based on modifying the position of thecommunication, may provide, to the user device, suggested self-supportactions to improve the position of the communication in the supportqueue, and/or may disconnect the communication with the user device whenthe one or more self-support actions performed by the user solve thesupport issue.

In some implementations, the support platform may provide, to the userdevice, information identifying movement in the support queue for theposition of the communication of the user, and may provide, to the userdevice, information indicating points awarded to the user for the one ormore self-support actions performed by the user.

In some implementations, the support platform may receive thecommunication associated with the support issue prior to receiving theinformation identifying the one or more self-support actions performedby the user, and may correlate the communication and the informationidentifying the one or more self-support actions performed by the userbased on a device identifier associated with the communication.

In some implementations, the support platform may receive thecommunication associated with the support issue after receiving theinformation identifying the one or more self-support actions performedby the user, and may correlate the communication and the informationidentifying the one or more self-support actions performed by the userbased on an account associated with the user.

In some implementations, the support platform may receive thecommunication associated with the support issue after receiving theinformation identifying the one or more self-support actions performedby the user, may provide, to the user device, an identifier foridentifying the one or more self-support actions performed by the user,may receive the identifier via the communication, and may correlate thecommunication and the information identifying the one or moreself-support actions performed by the user based on receiving theidentifier via the communication.

In some implementations, when performing the one or more actions, thesupport platform may cause a problem device to reboot, may cause theproblem device to execute a self-diagnostic action, may attempt tocommunicate with the problem device, may attempt to repair the problemdevice, may cause an autonomous vehicle to travel to a location of theuser, or may cause an unmanned aerial vehicle to travel to the locationof the user.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5. Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for utilizing machinelearning with self-support actions to determine support queue positionsfor support calls. In some implementations, one or more process blocksof FIG. 6 may be performed by a support platform (e.g., support platform220). In some implementations, one or more process blocks of FIG. 6 maybe performed by another device or a group of devices separate from orincluding the support platform, such as a user device (e.g., user device210).

As shown in FIG. 6, process 600 may include receiving communicationsassociated with support issues encountered by users of user devices(block 610). For example, the support platform (e.g., using computingresource 224, processor 320, communication interface 370, and/or thelike) may receive communications associated with support issuesencountered by users of user devices, as described above in connectionwith FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include receiving, from theuser devices, information identifying self-support actions performed bythe users in relation to the support issues (block 620). For example,the support platform (e.g., using computing resource 224, processor 320,storage component 340, communication interface 370, and/or the like) mayreceive, from the user devices, information identifying self-supportactions performed by the users in relation to the support issues, asdescribed above in connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include assigning thecommunications to positions in a support queue based on when thecommunications are received, wherein the support queue includesinformation identifying the positions of the communications, andinformation identifying when the communications are received (block630). For example, the support platform (e.g., using computing resource224, processor 320, memory 330, and/or the like) may assign thecommunications to positions in a support queue based on when thecommunications are received, as described above in connection with FIGS.1A-2. In some implementations, the support queue may include informationidentifying the positions of the communications, and informationidentifying when the communications are received.

As further shown in FIG. 6, process 600 may include associating theinformation identifying the self-support actions performed by the userswith the information identifying the positions of the communications(block 640). For example, the support platform (e.g., using computingresource 224, processor 320, storage component 340, and/or the like) mayassociate the information identifying the self-support actions performedby the users with the information identifying the positions of thecommunications, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include processing theinformation identifying the self-support actions performed by the users,with a model, to generate respective weights for the self-supportactions performed by the users (block 650). For example, the supportplatform (e.g., using computing resource 224, processor 320, memory 330,and/or the like) may process the information identifying theself-support actions performed by the users, with a model, to generaterespective weights for the self-support actions performed by the users,as described above in connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include associating therespective weights with the self-support actions performed by the users(block 660). For example, the support platform (e.g., using computingresource 224, processor 320, storage component 340, and/or the like) mayassociate the respective weights with the self-support actions performedby the users, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include generating scoresfor the communications based on associating the respective weights withthe self-support actions performed by the users (block 670). Forexample, the support platform (e.g., using computing resource 224,processor 320, memory 330, and/or the like) may generate scores for thecommunications based on associating the respective weights with theself-support actions performed by the users, as described above inconnection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include modifying thepositions of the communications, in the support queue, based on thescores (block 680). For example, the support platform (e.g., usingcomputing resource 224, processor 320, storage component 340, and/or thelike) may modify the positions of the communications, in the supportqueue, based on the scores, as described above in connection with FIGS.1A-2.

As further shown in FIG. 6, process 600 may include performing one ormore actions based on modifying the positions of the communications inthe support queue (block 690). For example, the support platform (e.g.,using computing resource 224, processor 320, memory 330, communicationinterface 370, and/or the like) may perform one or more actions based onmodifying the positions of the communications in the support queue, asdescribed above in connection with FIGS. 1A-2.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, when performing the one or more actions, thesupport platform may provide information indicating that one of thepositions of one of the communications in the support queue improved dueto one or more self-support actions performed by one of the users, mayprocess one of the communications of one of the users based on modifyingone of the positions of the one of the communications, may provide, toone of the user devices, suggested self-support actions to improve oneof the positions of one of the communications in the support queue,and/or may disconnect one of the communications with one of the userdevices when one of the self-support actions performed by one of theusers solves one of the support issues.

In some implementations, the support platform may provide, to one of theuser devices, information identifying movement in the support queue forone of the positions of one of the communications of one of the users,and may provide, to one of the user devices, information indicatingpoints awarded to one of the users for one or more of the self-supportactions performed by the one of the users.

In some implementations, the support platform may associate thecommunications and the information identifying the self-support actionsperformed by the users based on device identifiers associated with thecommunications. In some implementations, the support platform mayassociate the communications and the information identifying theself-support actions performed by the users based on accounts associatedwith the users.

In some implementations, the support platform may provide, to the userdevices, identifiers for identifying the self-support actions performedby the users, may receive the identifiers via the communications, andmay associate the communications and the information identifying theself-support actions performed by the users based on receiving theidentifiers via the communications.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, or the like.A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the phrase “only one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A method, comprising: receiving, by a device andfrom a first user device associated with a user, information identifyingone or more first actions performed via an account, each first action,of the one or more first actions, being a self-support action associatedwith an attempt to resolve an issue; receiving, by the device and from asecond user device associated with the user, a communication;correlating, by the device and based on the account and an identifier ofthe second user device, the one or more first actions with thecommunication; assigning, by the device and based on correlating the oneor more first actions with the communication, the communication to aposition in a queue; processing, by the device and with a machinelearning model, the information identifying the one or more firstactions to generate respective weights for the one or more firstactions; associating, by the device, the respective weights with the oneor more first actions; generating, by the device, a score for thecommunication based on associating the respective weights with the oneor more first actions; modifying, by the device, the position assignedto the communication in the queue based on the score; and performing, bythe device, one or more second actions based on modifying the positionassigned to the communication in the queue.
 2. The method of claim 1,further comprising: associating the account with the identifier; andwherein correlating the one or more first actions with the communicationcomprises: correlating the one or more first actions with thecommunication based on associating the account with the identifier. 3.The method of claim 1, wherein assigning the communication to theposition in the queue comprises: assigning the communication to theposition in the queue based on a time value associated with thecommunication.
 4. The method of claim 1, wherein assigning thecommunication to the position in the queue comprises: assigning thecommunication to the position in the queue based on the one or morefirst actions.
 5. The method of claim 1, further comprising: trainingthe machine learning model using historical self-support information. 6.The method of claim 1, further comprising: processing historicalself-support information into one or more of a training set, avalidation set, or a test set; and at least one of: training the machinelearning model using the training set, validating one or more results ofthe machine learning model using the validation set, or testing one ormore operations of the machine learning model using the test set.
 7. Themethod of claim 1, further comprising: associating the respectiveweights with one or more time values associated with the one or morefirst actions; and wherein generating the score comprises: generatingthe score based on associating the respective weights with the one ormore time values.
 8. A device, comprising: one or more memories; and oneor more processors coupled to the one or more memories, configured to:receive, from a first user device associated with a user, informationidentifying one or more first actions performed via an account, eachfirst action, of the one or more first actions, being a self-supportaction associated with an attempt to resolve an issue; receive, from asecond user device associated with the user, a communication; correlate,based on the account and an identifier of the second user device, theone or more first actions with the communication; assign, based oncorrelating the one or more first actions with the communication, thecommunication to a position in a queue; process, with a machine learningmodel, the information identifying the one or more first actions togenerate respective weights for the one or more first actions; associatethe respective weights with the one or more first actions; generate ascore for the communication based on associating the respective weightswith the one or more first actions; modify the position assigned to thecommunication in the queue based on the score; and perform one or moresecond actions based on modifying the position assigned to thecommunication in the queue.
 9. The device of claim 8, wherein the one ormore processors are further configured to: associate the account withthe identifier; and wherein the one or more processors, when correlatingthe one or more first actions with the identifier, are configured to:correlate the one or more first actions with the communication based onassociating the account with the identifier.
 10. The device of claim 8,wherein the one or more processors, when assigning the communication tothe position in the queue, are configured to: assign the communicationto the position in the queue based on a time value associated with thecommunication.
 11. The device of claim 8, wherein the one or moreprocessors, when assigning the communication to the position in thequeue, are configured to: assign the communication to the position inthe queue based on the one or more first actions.
 12. The device ofclaim 8, wherein the one or more processors are further configured to:train the machine learning model using historical self-supportinformation.
 13. The device of claim 8, wherein the one or moreprocessors are further configured to: process historical self-supportinformation into one or more of a training set, a validation set, or atest set; and at least one of: train the machine learning model usingthe training set, validate one or more results of the machine learningmodel using the validation set, or test one or more operations of themachine learning model using the test set.
 14. The device of claim 8,wherein the one or more processors are further configured to: associatethe respective weights with one or more time values associated with theone or more first actions; and wherein the one or more processors, whengenerating the score, are configured to: generate the score based onassociating the respective weights with the one or more time values. 15.A non-transitory computer-readable medium storing instructions, theinstructions comprising: one or more instructions that, when executed byone or more processors of a device, cause the one or more processors to:receive, from a first user device associated with a user, informationidentifying one or more first actions performed via an account, eachfirst action, of the one or more first actions, being a self-supportaction associated with an attempt to resolve an issue; receive, from asecond user device associated with the user, a communication; correlate,based on the account and an identifier of the second user device, theone or more first actions with the communication; assign, based oncorrelating the one or more first actions with the communication, thecommunication to a position in a queue; process, with a machine learningmodel, the information identifying the one or more first actions togenerate respective weights for the one or more first actions; associatethe respective weights with the one or more first actions; generate ascore for the communication based on associating the respective weightswith the one or more first actions; modify the position assigned to thecommunication in the queue based on the score; and perform one or moresecond actions based on modifying the position assigned to thecommunication in the queue.
 16. The non-transitory computer-readablemedium of claim 15, wherein the one or more instructions, when executedby the one or more processors, further cause the one or more processorsto: associate the account with the identifier; and wherein the one ormore instructions, that cause the one or more processors to correlatethe one or more first actions with the communication, cause the one ormore processors to: correlate the one or more first actions with thecommunication based on associating the account with the identifier. 17.The non-transitory computer-readable medium of claim 15, wherein the oneor more instructions, that cause the one or more processors to assignthe communication to the position in the queue, cause the one or moreprocessors to: assign the communication to the position in the queuebased on a time value associated with the communication.
 18. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, when executed by the one or more processors, furthercause the one or more processors to: train the machine learning modelusing historical self-support information.
 19. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions, when executed by the one or more processors, further causethe one or more processors to: process historical self-supportinformation into one or more of a training set, a validation set, or atest set; and at least one of: train the machine learning model usingthe training set, validate one or more results of the machine learningmodel using the validation set, or test one or more operations of themachine learning model using the test set.
 20. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions, when executed by the one or more processors, further causethe one or more processors to: associate the respective weights with oneor more time values associated with the one or more first actions; andwherein the one or more instructions, that cause the one or moreprocessors to generate the score, cause the one or more processors to:generate the score based on associating the respective weights with theone or more time values.